{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
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    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "DataAnalysis"
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 },
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import brown\n",
    "from tools import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "source": [
    "# Ch5 分类和标注词汇\n",
    "\n",
    "1.  什么是词汇分类，在自然语言处理中它们如何使用？\n",
    "2.  对于存储词汇和它们的分类来说什么是好的 Python 数据结构？\n",
    "3.  如何自动标注文本中每个词汇的词类？\n",
    "\n",
    "-   词性标注（parts-of-speech tagging，POS tagging）：简称标注。将词汇按照它们的词性（parts-of-speech，POS）进行分类并对它们进行标注\n",
    "-   词性：也称为词类或者词汇范畴。\n",
    "-   标记集：用于特定任务标记的集合。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Sec 5.2 标注语料库"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 5.2.1 表示已经标注的标识符"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "taggen_token=  ('fly', 'NN')\ntaggen_token[0]=  fly\ntaggen_token[1]=  NN\n"
     ]
    }
   ],
   "source": [
    "# 5.2.1 表示已经标注的标识符\n",
    "# str2tuple() 将已经标注的字符串转换成元组\n",
    "taggen_token = nltk.tag.str2tuple('fly/NN')\n",
    "print(\"taggen_token= \",taggen_token)\n",
    "print(\"taggen_token[0]= \",taggen_token[0])\n",
    "print(\"taggen_token[1]= \", taggen_token[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "sent.split()=  ['The/AT', 'grand/JJ', 'jury/NN', 'commented/VBD', 'on/IN', 'a/AT', 'number/NN', 'of/IN', 'other/AP', 'topics/NNS', ',/,', 'AMONG/IN', 'them/PPO', 'the/AT', 'Atlanta/NP', 'and/CC', 'Fulton/NP-tl', 'County/NN-tl', 'purchasing/VBG', 'departments/NNS', 'which/WDT', 'it/PPS', 'said/VBD', '``/``', 'ARE/BER', 'well/QL', 'operated/VBN', 'and/CC', 'follow/VB', 'generally/RB', 'accepted/VBN', 'practices/NNS', 'which/WDT', 'inure/VB', 'to/IN', 'the/AT', 'best/JJT', 'interest/NN', 'of/IN', 'both/ABX', 'governments/NNS', \"''/''\", './.']\n--------------- >split_sent_to_tuple< ---------------\n[('The', 'AT'), ('grand', 'JJ'), ('jury', 'NN'), ('commented', 'VBD'), ('on', 'IN'), ('a', 'AT'), ('number', 'NN'), ('of', 'IN'), ('other', 'AP'), ('topics', 'NNS'), (',', ','), ('AMONG', 'IN'), ('them', 'PPO'), ('the', 'AT'), ('Atlanta', 'NP'), ('and', 'CC'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ('purchasing', 'VBG'), ('departments', 'NNS'), ('which', 'WDT'), ('it', 'PPS'), ('said', 'VBD'), ('``', '``'), ('ARE', 'BER'), ('well', 'QL'), ('operated', 'VBN'), ('and', 'CC'), ('follow', 'VB'), ('generally', 'RB'), ('accepted', 'VBN'), ('practices', 'NNS'), ('which', 'WDT'), ('inure', 'VB'), ('to', 'IN'), ('the', 'AT'), ('best', 'JJT'), ('interest', 'NN'), ('of', 'IN'), ('both', 'ABX'), ('governments', 'NNS'), (\"''\", \"''\"), ('.', '.')]\n"
     ]
    }
   ],
   "source": [
    "# 拆分已经标注的语料文本为元组\n",
    "sent = '''\n",
    "The/AT grand/JJ jury/NN commented/VBD on/IN a/AT number/NN of/IN\n",
    "other/AP topics/NNS ,/, AMONG/IN them/PPO the/AT Atlanta/NP and/CC\n",
    "Fulton/NP-tl County/NN-tl purchasing/VBG departments/NNS which/WDT it/PPS\n",
    "said/VBD ``/`` ARE/BER well/QL operated/VBN and/CC follow/VB generally/RB\n",
    "accepted/VBN practices/NNS which/WDT inure/VB to/IN the/AT best/JJT\n",
    "interest/NN of/IN both/ABX governments/NNS ''/'' ./.\n",
    "'''\n",
    "\n",
    "print(\"sent.split()= \", sent.split())\n",
    "split_sent_to_tuple = [nltk.tag.str2tuple(t) for t in sent.split()]\n",
    "show_subtitle(\"split_sent_to_tuple\")\n",
    "print(split_sent_to_tuple)"
   ]
  },
  {
   "source": [
    "### 5.2.2 读取已经标注的语料库"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >brown.tagged_words()< ---------------\n",
      "[('The', 'AT'), ('Fulton', 'NP-TL'), ...]\n",
      "--------------- >brown.tagged_words(tagset='universal')< ---------------\n",
      "[('The', 'DET'), ('Fulton', 'NOUN'), ...]\n"
     ]
    }
   ],
   "source": [
    "# 打开brown语料库的ca01文件，可以看到下面的内容：\n",
    "# The/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at\n",
    "# investigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd\n",
    "# ``/`` no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.\n",
    "# 这个是已经标注好的语料库，可以使用函数tagged_words()直接读取\n",
    "\n",
    "# tagset='universal' 使用通用标注集进行词类标注\n",
    "show_subtitle(\"brown.tagged_words()\")\n",
    "print(brown.tagged_words())\n",
    "show_subtitle(\"brown.tagged_words(tagset='universal')\")\n",
    "print(brown.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.treebank.tagged_words()< ---------------\n",
      "[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]\n",
      "--------------- >nltk.corpus.treebank.tagged_words(tagset='universal')< ---------------\n",
      "[('Pierre', 'NOUN'), ('Vinken', 'NOUN'), (',', '.'), ...]\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.treebank.tagged_words()\")\n",
    "print(nltk.corpus.treebank.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.treebank.tagged_words(tagset='universal')\")\n",
    "print(nltk.corpus.treebank.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.nps_chat.tagged_words()< ---------------\n[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]\n--------------- >nltk.corpus.nps_chat.tagged_words(tagset='universal')< ---------------\n[('now', 'ADV'), ('im', 'PRON'), ('left', 'VERB'), ...]\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.nps_chat.tagged_words()\")\n",
    "print(nltk.corpus.nps_chat.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.nps_chat.tagged_words(tagset='universal')\")\n",
    "print(nltk.corpus.nps_chat.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.conll2000.tagged_words()< ---------------\n[('Confidence', 'NOUN'), ('in', 'ADP'), ('the', 'DET'), ...]\n--------------- >nltk.corpus.conll2000.tagged_words()< ---------------\n[('Confidence', 'NOUN'), ('in', 'ADP'), ('the', 'DET'), ...]\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.conll2000.tagged_words()\")\n",
    "print(nltk.corpus.conll2000.tagged_words(tagset='universal'))\n",
    "show_subtitle(\"nltk.corpus.conll2000.tagged_words()\")\n",
    "print(nltk.corpus.conll2000.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.sinica_treebank.tagged_words()< ---------------\n[('一', 'Neu'), ('友情', 'Nad'), ('嘉珍', 'Nba'), ...]\n--------------- >nltk.corpus.sinica_treebank.tagged_words(tagset='universal')< ---------------\n"
     ]
    }
   ],
   "source": [
    "# Note:以下的都无法正常转换为通用标注集\n",
    "# 因为每个语言的标注集都是根据自己的情况定义的，不与通用标注集完全相同\n",
    "\n",
    "show_subtitle(\"nltk.corpus.sinica_treebank.tagged_words()\")\n",
    "print(nltk.corpus.sinica_treebank.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.sinica_treebank.tagged_words(tagset='universal')\")\n",
    "# print(nltk.corpus.sinica_treebank.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.indian.tagged_words()< ---------------\n[('মহিষের', 'NN'), ('সন্তান', 'NN'), (':', 'SYM'), ...]\n--------------- >nltk.corpus.indian.tagged_words(tagset='universal')< ---------------\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.indian.tagged_words()\")\n",
    "print(nltk.corpus.indian.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.indian.tagged_words(tagset='universal')\")\n",
    "# print(nltk.corpus.indian.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.mac_morpho.tagged_words()< ---------------\n[('Jersei', 'N'), ('atinge', 'V'), ('média', 'N'), ...]\n--------------- >nltk.corpus.mac_morpho.tagged_words(tagset='universal')< ---------------\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.mac_morpho.tagged_words()\")\n",
    "print(nltk.corpus.mac_morpho.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.mac_morpho.tagged_words(tagset='universal')\")\n",
    "# print(nltk.corpus.mac_morpho.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >nltk.corpus.cess_cat.tagged_words()< ---------------\n",
      "[('El', 'da0ms0'), ('Tribunal_Suprem', 'np0000o'), ...]\n",
      "--------------- >nltk.corpus.cess_cat.tagged_words(tagset='universal')< ---------------\n"
     ]
    }
   ],
   "source": [
    "show_subtitle(\"nltk.corpus.cess_cat.tagged_words()\")\n",
    "print(nltk.corpus.cess_cat.tagged_words())\n",
    "show_subtitle(\"nltk.corpus.cess_cat.tagged_words(tagset='universal')\")\n",
    "# print(nltk.corpus.cess_cat.tagged_words(tagset='universal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >brown.tagged_sents()[0]< ---------------\n",
      "[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ('Grand', 'JJ-TL'), ('Jury', 'NN-TL'), ('said', 'VBD'), ('Friday', 'NR'), ('an', 'AT'), ('investigation', 'NN'), ('of', 'IN'), (\"Atlanta's\", 'NP$'), ('recent', 'JJ'), ('primary', 'NN'), ('election', 'NN'), ('produced', 'VBD'), ('``', '``'), ('no', 'AT'), ('evidence', 'NN'), (\"''\", \"''\"), ('that', 'CS'), ('any', 'DTI'), ('irregularities', 'NNS'), ('took', 'VBD'), ('place', 'NN'), ('.', '.')]\n",
      "--------------- >brown.tagged_sents()[1]< ---------------\n",
      "[('The', 'AT'), ('jury', 'NN'), ('further', 'RBR'), ('said', 'VBD'), ('in', 'IN'), ('term-end', 'NN'), ('presentments', 'NNS'), ('that', 'CS'), ('the', 'AT'), ('City', 'NN-TL'), ('Executive', 'JJ-TL'), ('Committee', 'NN-TL'), (',', ','), ('which', 'WDT'), ('had', 'HVD'), ('over-all', 'JJ'), ('charge', 'NN'), ('of', 'IN'), ('the', 'AT'), ('election', 'NN'), (',', ','), ('``', '``'), ('deserves', 'VBZ'), ('the', 'AT'), ('praise', 'NN'), ('and', 'CC'), ('thanks', 'NNS'), ('of', 'IN'), ('the', 'AT'), ('City', 'NN-TL'), ('of', 'IN-TL'), ('Atlanta', 'NP-TL'), (\"''\", \"''\"), ('for', 'IN'), ('the', 'AT'), ('manner', 'NN'), ('in', 'IN'), ('which', 'WDT'), ('the', 'AT'), ('election', 'NN'), ('was', 'BEDZ'), ('conducted', 'VBN'), ('.', '.')]\n"
     ]
    }
   ],
   "source": [
    "# 使用tagged_sents()可以直接把语料库分割成句子，而不是将所有的词表示成一个链表，句子中的词同样进行了词类标注。\n",
    "# 因为开发的自动标注器需要在句子链表上进行训练和测试，而不是在词链表上。\n",
    "show_subtitle(\"brown.tagged_sents()[0]\")\n",
    "print(brown.tagged_sents()[0])\n",
    "show_subtitle(\"brown.tagged_sents()[1]\")\n",
    "print(brown.tagged_sents()[1])"
   ]
  },
  {
   "source": [
    "### 5.2.3 A Universal Part-of-Speech Tagset, 一个通用的（简化的）标注集\n",
    "http://www.nltk.org/book/ch05.html Table2.1 （比书P200 表5-1还要简单）\n",
    "\n",
    "| Tag  | Meaning             | English Examples                       |\n",
    "| ---- | ------------------- | -------------------------------------- |\n",
    "| Tag  | Meaning             | English Examples                       |\n",
    "| ADJ  | adjective           | new, good, high, special, big, local   |\n",
    "| ADP  | adposition          | on, of, at, with, by, into, under      |\n",
    "| ADV  | adverb              | really, already, still, early, now     |\n",
    "| CONJ | conjunction         | and, or, but, if, while, although      |\n",
    "| DET  | determiner, article | the, a, some, most, every, no, which   |\n",
    "| NOUN | noun                | year, home, costs, time, Africa        |\n",
    "| NUM  | numeral             | twenty-four, fourth, 1991, 14:24       |\n",
    "| PRT  | particle            | at, on, out, over per, that, up, with  |\n",
    "| PRON | pronoun             | he, their, her, its, my, I, us         |\n",
    "| VERB | verb                | is, say, told, given, playing, would   |\n",
    "| .    | punctuation marks   | . , ; !                                |\n",
    "| X    | other               | ersatz, esprit, dunno, gr8, univeristy |"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "list(tag_fd)=  ['NOUN', 'VERB', 'ADP', '.', 'DET', 'ADJ', 'ADV', 'CONJ', 'PRON', 'PRT', 'NUM', 'X']\ntag_fd.keys()=  dict_keys(['DET', 'NOUN', 'ADJ', 'VERB', 'ADP', '.', 'ADV', 'CONJ', 'PRT', 'PRON', 'NUM', 'X'])\ntag_fd.most_common()=  [('NOUN', 30654), ('VERB', 14399), ('ADP', 12355), ('.', 11928), ('DET', 11389), ('ADJ', 6706), ('ADV', 3349), ('CONJ', 2717), ('PRON', 2535), ('PRT', 2264), ('NUM', 2166), ('X', 92)]\n--------------- >tag_fd.tabulate()< ---------------\n NOUN  VERB   ADP     .   DET   ADJ   ADV  CONJ  PRON   PRT   NUM     X \n30654 14399 12355 11928 11389  6706  3349  2717  2535  2264  2166    92 \n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n",
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\n"
     },
     "metadata": {
      "needs_background": "dark"
     }
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc2a5b00>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "# 布朗语料库的新闻类中哪些标记最常见\n",
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n",
    "print(\"list(tag_fd)= \", list(tag_fd))\n",
    "print(\"tag_fd.keys()= \",tag_fd.keys())\n",
    "print(\"tag_fd.most_common()= \",tag_fd.most_common())\n",
    "show_subtitle(\"tag_fd.tabulate()\")\n",
    "tag_fd.tabulate()\n",
    "tag_fd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图形化的POS一致性工具，可以用来寻找任一词和POS标记的组合\n",
    "# 例如：\"VERB VERB\" 或者 \"was missing\" 或者 \"had VERB\" 或者 \"DET money\" 等等\n",
    "nltk.app.concordance()"
   ]
  },
  {
   "source": [
    "### 5.2.4 名词\n",
    "名词：一般指人、地点、事情和概念。可能出现在限定词和形容词之后，可以是动词的主语或者宾语。\n",
    "\n",
    "表5-2 名词的句法模式"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "fdist.most_common()=  [('NOUN', 7959), ('DET', 7373), ('ADJ', 4761), ('ADP', 3781), ('.', 2796), ('VERB', 1842), ('CONJ', 938), ('NUM', 894), ('ADV', 186), ('PRT', 94), ('PRON', 19), ('X', 11)]\ntag_list=  ['NOUN', 'DET', 'ADJ', 'ADP', '.', 'VERB', 'CONJ', 'NUM', 'ADV', 'PRT', 'PRON', 'X']\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n",
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\n"
     },
     "metadata": {
      "needs_background": "dark"
     }
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1d50b8>"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# 统计构成二元模型（W1，W2）中W2=‘NOUN’的W1的词性的比例\n",
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "word_tag_pairs = nltk.bigrams(brown_news_tagged)  # 构建双词链表\n",
    "noun_precedes = [\n",
    "        a[1]\n",
    "        for (a, b) in word_tag_pairs\n",
    "        if b[1] == 'NOUN'\n",
    "]\n",
    "fdist = nltk.FreqDist(noun_precedes)\n",
    "print(\"fdist.most_common()= \", fdist.most_common())\n",
    "tag_list = [\n",
    "        tag\n",
    "        for (tag, _) in fdist.most_common()\n",
    "]\n",
    "print(\"tag_list= \", tag_list)\n",
    "fdist.plot()\n",
    "# 结论：名词最容易出现在名词后面\n",
    "# 其次是出现在限定词和形容词之后，包括数字形容词（即数词，标注为NUM）"
   ]
  },
  {
   "source": [
    "# 5.2.5 动词\n",
    "动词：描述事件和行动的词。在句子中，动词通常表示涉及一个或多个名词短语所指示物的关系。\n",
    "\n",
    "表5-3 动词的句法模式"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >word_tag_fd.most_common(20)< ---------------\n[((',', '.'), 4885), (('the', 'DET'), 4038), (('.', '.'), 3828), (('of', 'ADP'), 2319), (('to', 'PRT'), 2161), (('a', 'DET'), 1874), (('in', 'ADP'), 1554), (('and', 'CONJ'), 1505), (('*-1', 'X'), 1123), (('0', 'X'), 1099), (('*', 'X'), 965), (('for', 'ADP'), 815), (('*T*-1', 'X'), 806), ((\"'s\", 'PRT'), 761), (('*U*', 'X'), 744), (('$', '.'), 718), (('The', 'DET'), 713), (('``', '.'), 702), ((\"''\", '.'), 684), (('is', 'VERB'), 671)]\n--------------- >word_tag_list[:20]< ---------------\n['is', 'said', 'was', 'are', 'be', 'has', 'have', 'will', 'says', 'would', 'were', 'had', 'been', 'could', \"'s\", 'can', 'do', 'say', 'make', 'may']\n--------------- >fdist.most_common(20)< ---------------\n[((',', '.'), 4885), (('the', 'DET'), 4038), (('.', '.'), 3828), (('of', 'ADP'), 2319), (('to', 'PRT'), 2161), (('a', 'DET'), 1874), (('in', 'ADP'), 1554), (('and', 'CONJ'), 1505), (('*-1', 'X'), 1123), (('0', 'X'), 1099), (('*', 'X'), 965), (('for', 'ADP'), 815), (('*T*-1', 'X'), 806), ((\"'s\", 'PRT'), 761), (('*U*', 'X'), 744), (('$', '.'), 718), (('The', 'DET'), 713), (('``', '.'), 702), ((\"''\", '.'), 684), (('is', 'VERB'), 671)]\n"
     ]
    }
   ],
   "source": [
    "# 找出新闻文本中最常见的动词（频率分布中计算的项目是词——标记对）\n",
    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
    "word_tag_fd = nltk.FreqDist(wsj)\n",
    "show_subtitle(\"word_tag_fd.most_common(20)\")\n",
    "print(word_tag_fd.most_common(20))\n",
    "word_tag_list = [\n",
    "        wt[0]\n",
    "        for (wt, _) in word_tag_fd.most_common()\n",
    "        if wt[1] == 'VERB'\n",
    "]\n",
    "show_subtitle(\"word_tag_list[:20]\")\n",
    "print(word_tag_list[:20])\n",
    "\n",
    "# 对做过一次频率统计的输出的结果再做一次频率统计结果不变\n",
    "fdist = nltk.FreqDist(word_tag_fd)\n",
    "show_subtitle(\"fdist.most_common(20)\")\n",
    "print(fdist.most_common(20))\n",
    "# fdist.plot(cumulative=True)   # 不能执行，会死机，因为动词单词数目太多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "fdist.most_common()=  [('NOUN', 4240), ('VERB', 2292), ('X', 1354), ('PRON', 1329), ('PRT', 1291), ('ADV', 1093), ('.', 1040), ('CONJ', 355), ('DET', 346), ('ADP', 82), ('ADJ', 77), ('NUM', 65)]\n--------------- >fdist.tabulate()< ---------------\nNOUN VERB    X PRON  PRT  ADV    . CONJ  DET  ADP  ADJ  NUM \n4240 2292 1354 1329 1291 1093 1040  355  346   82   77   65 \n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n",
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\n"
     },
     "metadata": {
      "needs_background": "dark"
     }
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd8180b8>"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
    "word_tag_pairs = nltk.bigrams(wsj)\n",
    "verb_preceders = [\n",
    "        a[1]\n",
    "        for (a, b) in word_tag_pairs\n",
    "        if b[1] == 'VERB'\n",
    "]\n",
    "fdist = nltk.FreqDist(verb_preceders)\n",
    "print(\"fdist.most_common()= \", fdist.most_common())\n",
    "show_subtitle(\"fdist.tabulate()\")\n",
    "fdist.tabulate()\n",
    "fdist.plot()\n",
    "# 结论：动词出现在名词、动词、名字和副词后面。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "list(cfd1)[:20]=  ['Pierre', 'Vinken', ',', '61', 'years', 'old', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.', 'Mr.', 'is', 'chairman']\ncfd1['yield'].most_common(20)=  [('VERB', 28), ('NOUN', 20)]\ncfd1['cut'].most_common(20)=  [('VERB', 25), ('NOUN', 3)]\n"
     ]
    }
   ],
   "source": [
    "# 因为词汇和标记是成对的\n",
    "# 所以把词汇作为条件，把标记作为事件，使用条件——事件对的链表初始化条件频率分布。\n",
    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
    "cfd1 = nltk.ConditionalFreqDist(wsj)\n",
    "print(\"list(cfd1)[:20]= \", list(cfd1)[:20])\n",
    "print(\"cfd1['yield'].most_common(20)= \", cfd1['yield'].most_common(20))\n",
    "print(\"cfd1['cut'].most_common(20)= \", cfd1['cut'].most_common(20))\n",
    "# 动词太多，执行时间过长，显示效果不好\n",
    "# cfd1.tabulate()\n",
    "# cfd1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cfd2=  <ConditionalFreqDist with 46 conditions>\ncfd2['VBN']=  <FreqDist with 692 samples and 2134 outcomes>\n--------------- >cfd2['VBN'].most_common(20)< ---------------\n[('been', 150), ('expected', 52), ('made', 33), ('compared', 32), ('based', 31), ('used', 26), ('priced', 26), ('sold', 25), ('named', 20), ('designed', 20), ('held', 18), ('fined', 18), ('taken', 16), ('paid', 15), ('traded', 15), ('increased', 14), ('said', 14), ('filed', 13), ('reached', 13), ('called', 13)]\n--------------- >list(cfd2['VBN'].keys())[:20]< ---------------\n['named', 'used', 'caused', 'exposed', 'reported', 'replaced', 'sold', 'died', 'expected', 'diagnosed', 'studied', 'industrialized', 'owned', 'found', 'classified', 'rejected', 'outlawed', 'imported', 'tracked', 'thought']\n--------------- >cfd2['VBN'].most_common(20)< ---------------\n[('been', 150), ('expected', 52), ('made', 33), ('compared', 32), ('based', 31), ('used', 26), ('priced', 26), ('sold', 25), ('named', 20), ('designed', 20), ('held', 18), ('fined', 18), ('taken', 16), ('paid', 15), ('traded', 15), ('increased', 14), ('said', 14), ('filed', 13), ('reached', 13), ('called', 13)]\n--------------- >in< ---------------\n'been' in cfd2['VBN'].keys()=  True\n"
     ]
    }
   ],
   "source": [
    "# 也可以颠倒配对，把标记作为条件，词汇作为事件，生成条件频率分布，就可以直接查找标记对应哪些词了。\n",
    "wsj = nltk.corpus.treebank.tagged_words()\n",
    "cfd2 = nltk.ConditionalFreqDist((tag, word) for (word, tag) in wsj)\n",
    "print(\"cfd2= \", cfd2)\n",
    "print(\"cfd2['VBN']= \", cfd2['VBN'])\n",
    "show_subtitle(\"cfd2['VBN'].most_common(20)\")\n",
    "print(cfd2['VBN'].most_common(20))\n",
    "show_subtitle(\"list(cfd2['VBN'].keys())[:20]\")\n",
    "print(list(cfd2['VBN'].keys())[:20])\n",
    "show_subtitle(\"cfd2['VBN'].most_common(20)\")\n",
    "print(cfd2['VBN'].most_common(20))\n",
    "show_subtitle(\"in\")\n",
    "print(\"'been' in cfd2['VBN'].keys()= \", 'been' in cfd2['VBN'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >cfd3.conditions()[:20]< ---------------\n",
      "['Pierre', 'Vinken', ',', '61', 'years', 'old', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.', 'Mr.', 'is', 'chairman']\n",
      "--------------- >list(cfd3.keys())[:20]< ---------------\n",
      "['Pierre', 'Vinken', ',', '61', 'years', 'old', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive', 'director', 'Nov.', '29', '.', 'Mr.', 'is', 'chairman']\n",
      "--------------- >word_list[:20]< ---------------\n",
      "['named', 'used', 'caused', 'reported', 'said', 'stopped', 'heard', 'studied', 'led', 'replaced', 'sold', 'worked', 'died', 'found', 'rejected', 'imposed', 'made', 'dumped', 'imported', 'mixed']\n",
      "--------------- >idx1< ---------------\n",
      "stock 0 they had *T*-1 . While program trades swiftly kicked in , a `` circuit breaker '' that *T*-1\n",
      "--------------- >idx2< ---------------\n",
      "-- the first time 0 a head of state has kicked off an issue at the Big Board here *T*-1\n"
     ]
    }
   ],
   "source": [
    "# 尝试分辨VD（过去式）和VN（过去分词）之间的区别\n",
    "# 先找出同是VD和VN的词汇，然后分析它们的上下文区别\n",
    "wsj = nltk.corpus.treebank.tagged_words()\n",
    "cfd3 = nltk.ConditionalFreqDist(wsj)\n",
    "# cfd.conditions() 返回所有的条件构成的链表，等价于list(cfd1.keys())返回所有的关键字。\n",
    "show_subtitle(\"cfd3.conditions()[:20]\")\n",
    "print(cfd3.conditions()[:20])\n",
    "show_subtitle(\"list(cfd3.keys())[:20]\")\n",
    "print(list(cfd3.keys())[:20])\n",
    "# 寻找既可以作为 VBD 还可以作为 VBN 的单词\n",
    "word_list = [\n",
    "        w\n",
    "        for w in cfd3.conditions()\n",
    "        if 'VBD' in cfd3[w] and 'VBN' in cfd3[w]\n",
    "]\n",
    "show_subtitle(\"word_list[:20]\")\n",
    "print(word_list[:20])\n",
    "# kicked 作为 VBD 的示例句子\n",
    "idx1 = wsj.index(('kicked', 'VBD'))\n",
    "show_subtitle(\"idx1\")\n",
    "print(' '.join(word for word, tag in wsj[idx1 - 10:idx1 + 10]))\n",
    "# kicked 作为 VBN 的示例句子\n",
    "idx2 = wsj.index(('kicked', 'VBN'))\n",
    "show_subtitle(\"idx2\")\n",
    "print(' '.join(word for word, tag in wsj[idx2 - 10:idx2 + 10]))"
   ]
  },
  {
   "source": [
    "### 5.2.6 其他词类\n",
    "（形容词、副词、介词、冠词（限定词）、情态动词、人称代词）\n",
    "-   形容词：修饰名词，可以作为修饰符 或 谓语。\n",
    "-   副词：修饰动词，指定时间、方式、地点或动词描述的事件发展方向；修饰形容词。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 5.2.7 未简化的标记(P204)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "NN [('year', 137), ('time', 97), ('state', 88), ('week', 85), ('man', 72)]\nNN$ [(\"year's\", 13), (\"world's\", 8), (\"state's\", 7), (\"nation's\", 6), (\"city's\", 6)]\nNN$-HL [(\"Golf's\", 1), (\"Navy's\", 1)]\nNN$-TL [(\"President's\", 11), (\"Administration's\", 3), (\"Army's\", 3), (\"League's\", 3), (\"University's\", 3)]\nNN-HL [('sp.', 2), ('problem', 2), ('Question', 2), ('cut', 2), ('party', 2)]\nNN-NC [('ova', 1), ('eva', 1), ('aya', 1)]\nNN-TL [('President', 88), ('House', 68), ('State', 59), ('University', 42), ('City', 41)]\nNN-TL-HL [('Fort', 2), ('Mayor', 1), ('Commissioner', 1), ('City', 1), ('Oak', 1)]\nNNS [('years', 101), ('members', 69), ('people', 52), ('sales', 51), ('men', 46)]\nNNS$ [(\"children's\", 7), (\"women's\", 5), (\"men's\", 3), (\"janitors'\", 3), (\"taxpayers'\", 2)]\nNNS$-HL [(\"Dealers'\", 1), (\"Idols'\", 1)]\nNNS$-TL [(\"Women's\", 4), (\"States'\", 3), (\"Giants'\", 2), (\"Princes'\", 1), (\"Bombers'\", 1)]\nNNS-HL [('Wards', 1), ('deputies', 1), ('bonds', 1), ('aspects', 1), ('Decisions', 1)]\nNNS-TL [('States', 38), ('Nations', 11), ('Masters', 10), ('Communists', 9), ('Rules', 9)]\nNNS-TL-HL [('Nations', 1)]\n"
     ]
    }
   ],
   "source": [
    "# Ex5-1 找出最频繁的名词标记的程序\n",
    "def find_tags(tag_prefix, tagged_text):\n",
    "    cfd = nltk.ConditionalFreqDist(\n",
    "            (tag, word)\n",
    "            for (word, tag) in tagged_text\n",
    "            if tag.startswith(tag_prefix))\n",
    "    return dict(\n",
    "            (tag, cfd[tag].most_common(5))\n",
    "            for tag in cfd.conditions()\n",
    "    )\n",
    "\n",
    "\n",
    "brown_tagged_words = brown.tagged_words(categories='news')\n",
    "tag_dict = find_tags('NN', brown_tagged_words)\n",
    "for tag in sorted(tag_dict):\n",
    "    print(tag, tag_dict[tag])"
   ]
  },
  {
   "source": [
    "### 5.2.8 探索已经标注的语料库"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[',', '.', 'accomplished', 'analytically', 'appear', 'apt', 'associated', 'assuming', 'became', 'become', 'been', 'began', 'call', 'called', 'carefully', 'chose', 'classified', 'colorful', 'composed', 'contain', 'differed', 'difficult', 'encountered', 'enough', 'equate', 'extremely', 'found', 'happens', 'have', 'ignored', 'in', 'involved', 'more', 'needed', 'nightly', 'observed', 'of', 'on', 'out', 'quite', 'represent', 'responsible', 'revamped', 'seclude', 'set', 'shortened', 'sing', 'sounded', 'stated', 'still', 'sung', 'supported', 'than', 'to', 'when', 'work']\n"
     ]
    }
   ],
   "source": [
    "# 观察 often 后面的词汇\n",
    "brown_learned_text = brown.words(categories='learned')\n",
    "print(sorted(set(\n",
    "        b\n",
    "        for (a, b) in nltk.bigrams(brown_learned_text)\n",
    "        if a == 'often'\n",
    ")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "brown_learned_bigrams=  <generator object bigrams at 0x00000000082C7A40>\n",
      "--------------- >a_b_list< ---------------\n",
      "[(('1', 'NUM'), ('.', '.')), (('.', '.'), ('Introduction', 'NOUN')), (('Introduction', 'NOUN'), ('It', 'PRON')), (('It', 'PRON'), ('has', 'VERB')), (('has', 'VERB'), ('recently', 'ADV')), (('recently', 'ADV'), ('become', 'VERB')), (('become', 'VERB'), ('practical', 'ADJ')), (('practical', 'ADJ'), ('to', 'PRT')), (('to', 'PRT'), ('use', 'VERB')), (('use', 'VERB'), ('the', 'DET')), (('the', 'DET'), ('radio', 'NOUN')), (('radio', 'NOUN'), ('emission', 'NOUN')), (('emission', 'NOUN'), ('of', 'ADP')), (('of', 'ADP'), ('the', 'DET')), (('the', 'DET'), ('moon', 'NOUN')), (('moon', 'NOUN'), ('and', 'CONJ')), (('and', 'CONJ'), ('planets', 'NOUN')), (('planets', 'NOUN'), ('as', 'ADP')), (('as', 'ADP'), ('a', 'DET')), (('a', 'DET'), ('new', 'ADJ'))]\n"
     ]
    }
   ],
   "source": [
    "brown_learned_tagged = brown.tagged_words(categories='learned', tagset='universal')\n",
    "brown_learned_bigrams = nltk.bigrams(brown_learned_tagged)\n",
    "print(\"brown_learned_bigrams= \",brown_learned_bigrams)\n",
    "a_b_list = [\n",
    "        (a, b)\n",
    "        for (a, b) in brown_learned_bigrams\n",
    "]\n",
    "show_subtitle(\"a_b_list\")\n",
    "print(a_b_list[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "VERB  ADV  ADP  ADJ    .  PRT \n  37    8    7    6    4    2 \n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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/lwhV6xWPIuKwcML/n4H3sV6+Uow1P8/v21mU2x3Zs4++rm4Kb7mZeUWFTpcjIh4UTvjnA4FRvwewZuuUCA0PDHBox24A1umefxFxQDjhn8zYMf5UYLY95XjHyNBP2WOb8CVE8qC1iEjkwkmdbwGvAn8IPAXsBn5oY02ecPrgYVrONpKZl0vJbRO/cU1EZDqFE/6/An4Xa0qHBOBzwHfsLMorqoJH/xr6EZFoC3e8oQ4r8L+N9aIWmQY1L2wHYMUD95GSrrtnRSR6NNjsoPam89S/U8OMmamseuRBp8sREQ9R+DusWkM/IuIAhb/DDu/ay0BvH4vLVpO9cIHT5YiIRyj8HTbQ28uRV/cCmuxNRKLHrvBPBT4LnMF6BwBYE8MdB/YCz9m03bgUuud/yyZ8Pp/D1YiIF9gV/nnAAPCzq5Z/HVgPbLVpu3Gp/p0a2pvPk72wgKKy1U6XIyIeYFf4NwDPAkOjll3GOht4A+uBMQkyxlAdvO1znSZ7E5Eo8Blj7Pz+p7GGfn44alkqsA/4BHDiqvUrgo2Ojo6ybdu2RbTRwsJCGhoaIvqsUwKpM+hduwz8ftKrjuILTO7fJR77PFXqszeoz5NTUVFRA0w8bYAxxs72tDHmiauWJRtj3jHGFIz32aqqKgNE1CorKyP+rJPtqZ98z3zzyAFT9thGz/R5Kk199kZTnyfXjDHV4eRzEvZYCPwbUIA19v8AcBTYEPz7Z4Amm7Ydt6qef5lFq1dSvnUzNS++4nQ5IuJidoV/I9c+7fi6TdtzhfdeeZXH/+TzFN9axpy8eVy+cNHpkkTEpXSffwzp7+rm/T37SEhIoGyL7vkXEfso/GPMlVc8KvxFxD4K/xhTe6CKzkstzCsq5MZbbna6HBFxKYV/jAn4/dS8uAPQPf8iYh+FfwwaGfpZvekhkmbMcLgaEXEjhX8MOl9/ig+OHictI4Ob77/H6XJExIUU/jGq+jld+BUR+yj8Y9TBl3cyPDTE0jtvY3Z2ltPliIjLKPxjVM/lDo7t209iUhJrH90w8QdERCZB4R/DQvf86xWPIjLNFP4x7Ni+/fS0X6agtJgFy0qdLkdEXEThH8P8w8O8+/JOAMp1z7+ITCOFf4wbGfpZ++gjJCbZNQ+fiHiNwj/GNR49QXPdSWZlzWXZPXc4XY6IuITCPw5cuedfQz8iMj0U/nGg5qUdBPx+brrvLtIyM5wuR0RcQOEfB7paWjmx/22SkpNZs/kRp8sRERdQ+McJTfcgItNJ4R8n3n/tDfo6u7hxxU3kLSlyuhwRiXMK/zgxPDjIezt2A5rnX0SmTuEfR0aGftY+toGExESHqxGReKbwjyNnDh3h0pmzZM7LpeT2dU6XIyJxzK7wTwU+C5wBngguuxXYBxwAnrFpu65XFXzid50u/IrIFNgV/nnAAPCzUcueBT4O3IG1I7jTpm27Ws0LrxAIBFjxwH2kzkp3uhwRiVM+Y4yd3/801tH/c8Bu4Dbgr4D7gJ8D375q/Ypgo6Ojo2zbtm0RbbSwsJCGhoaIPhsP+m4qwj9nNin1jSRfbAPc3+drUZ+9QX2enIqKihqgfMIVjTF2tqeNMU8YY7KMMUeNMZXGmDXGmKeMMZ8b77NVVVUGiKhVVlZG/Nl4aGVbNplvHjlgPvuj73qmz178d1af1edImjGmOpx8jtYF3zZgBvA14D1gM7A/Stt2nSOv7mWgt5eitavIuXGh0+WISByyK/wXAtVYQzhfBn4c/PMvuXLRt8qmbbveYF8fh3buATTZm4hExq7wb8QacyoAioBPAnuAdcA9wFdt2q5njNzzX7ZlIz6fz+FqRCTe6D7/OHWq5j3azjWTVZDP4vI1TpcjInFG4R+njDFUv7AdgHV6wbuITJLCP45VP2+F/y0P349J0D+liIRPiRHHWj9o5PS7h0hJS2M4Wy95EZHwKfzj3Mh0D8PzshyuRETiSZLTBcjUHNqxm//wpS9A5iz+y7P/h67WVjpbWulubaOzpY2ulla6Wlvpam2jp70DEwg4XbKIxACFf5zr7+6h+sXt3PFbj1Ny+/hPdAf8frrb2ulqaaOrtc3aKbS00tnSFtxZtAZ3Fm30dXZFqQci4gSFvwv861e/wQPFy/nWd/6BWdlZZGRnMzsnm9k5WczOzrL+nJ3FrKy5ZOTmkJGbM+F3Dg8OWjuI0NlDcIfR8uEzi8G+vij0UkSmk8LfBYwxJPYOUHtg/IemE5ISmTV3rrVTyMkmIzubWdlZzM7JIiMnO7STmJ2dxcyM2czNn8/c/PkTbn+gt/e6O4rRZxZdrW0MDw5OV7dFZAoU/h4SGPbTeamFzkstE66blJLC7Oy5V84ccrLJyM4K7iyygzuLLGZnZ5OSlkZKWho5N0w8z1BvZ+eVYaeW1uCfW8f83tnSSk/7ZQJ+/3R0W0SuQeEv1zQ8MEB703nam85PuG5KWlrobCJ09pBzZfhpVvZca2eRnU1aRgZpGRnkLV407ncGAgF62i9f2SmMObNopau1HX9aCulzMunt6MTmqclFXEfhL1M20NvLwNleWs42jruez+djZsbsMWcToesS2dlkBHcgs4LXJ0aGoCgtvub39QFffeMV/EPDdLe10xm8gD3mzOKqn/3dPTb8FxCJPwp/iRpjDL0dnfR2dHLh1Jlx101ITCR9TuaoM4nsK2cXwZ1C6coV9AwNkpaZQWZeLpl5uRPWMNQ/MGqHcPWdTmOHoIb6B6ap5yKxR+EvMSng94cuHF9PZWUlX3jySRKTk5mdNXfUsNPcsUNQo3YeqenpZC3IJ2tB/oQ19Hf3THinU1drK92t7fiHh6ez+yK2U/hL3PMPDXH5wkUuX7g44bozZqaOuh0268NnFtlXhqJSZ6WTOiud3EU3Tvi9PZc7rnmnU1dre2in0dXaqgftJGYo/MVTBvv6aWtsoq2xacJ1U2fPCl6ozrrudYrZOdb1ifQ5maTPyWR+8eJxvzPg99PdfnnstYnW1rG3yQZvj+3r7Jyubot8iMJf5Dr6u7rp7+rm4unxX6Tt8/lIC16fyBg5e7jOdYpZWdadTxk52RNuf3hoaMz1iO7WNgYXzGP5vXfRdKKWjguXpqur4kEKf5EpMsbQ036ZnvbLnK87Oe66CUmJzMrKCl2X+NDtsFc9aDdnfh5z5ueFPj8I/N4/PANYQ01Nx+toqq3j3PE6mk7UcfHUGV1/kLAo/EWiKDDsp/PiJTovTnzUHnrQLrhDyMjJ4Xd+71McPdtAwbIS0udkUnJ7+Zg5nYaHhrhw8jRNJ+poOlHPueO1NJ2o1xCSfIjCXyRGXetBuyce2sh3n3wKgMy8XApKSyhYVsKCZaUUlBaTfeNCFiwrZcGy0jHf1d58nqYT9cGdgnWm0NZ4Tg/HeZjCXyROdVy4RMeFSxx7Y39o2YyZM8kvXULB0hIKlpawYGkJ80uWhOZpunn93aF1+3t6aK49GdohNJ2oo7nupJ5v8Agnwr8feCv4528D/8+BGkRcabCvj4ZD79Nw6P3QMl9CAjk3LgztEAqWFrNgaSmZebkUrbmFojW3hNYN+P20nG0MDRc1nbB+hjMflMQXJ8L/PLDege2KeJIJBLh05iyXzpzl0I7doeXpc+dYO4PSYgqWWTuGvKJFzCsqZF5RIWs2PRxat6u1jebaeus6QnCHcPH0GQLDmnwvXvkcGPOrB5qAFuC/AadH/V1FsNHR0VG2bdu2iDZQWFhIQ8P4t+e5jfrsDXb32fh8BNJSCKTPJJCWij99JoH0VEi6xnFiIEBC7wAJPX0k9PaR2NNPQk8fPv/0PsSmf+fJqaioqAHGf7MTzoT/iN8APgr8zrX+srq62qxbty6iL66srOTJJ5+cQmnxR332Bqf6PDd/PgVLiykIXlguWFZy3Sm8284101RbZ92GeqKOcyfqaD/XHPHFZf07T44xJqzwd/KCrw9od3D7IhKm9ubztDef59d73wwtS0lPs3YEoWsJJeSXLAnNnbTi/ntD6/Z391gXlWvraQpeT2iuP8XwgC4uOyXa4T8P+FdgALgI/EGUty8i02Sgp5fTBw9z+uDh0LKExERygrebFiwtpmCp9TMjN4fFZatZXLY6tG7A7+fimbNX7jYKnimMN5mfTJ9oh/9F4J4ob1NEoiTg93PxdAMXTzdwcPuu0PJZ2XMpKC1hwbIrZwm5i25k/pIi5i8pYu3mR0Lrdra00hwcLmo6UU9gZgoJiYl6s9s0033+ImK77tZ2ag+8Q+2Bd0LLklJSmL+kyHoeYVkJ+cFbUEfmPlp61+0A9AJ/89ZumutP0nyiPrhTqKO5tl4v55kChb+IOGJ4YIDGo8dpPHo8tMzn8zF3Qf6Vs4RlJay843aSU1O4ccVN3LjipjHf0dp4jnPH60adKdSF9epRUfiLSAwxxoSm3H5/z+uAdefL5//4i+SXFlNQWsyCZaXkLy0mv3gx2QsXkL1wAbc8tD70Hb2dnTTXngw+qGbtEM7Xn8Y/NORQr2KTwl9EYl5fZxenqg9yqvpgaFlCUiLzFhVaD6gFzxTyS4uZnZ3FkvI1LClfE1rXPzTMxTMNnDteO2boqKf9shPdiQkKfxGJS4FhP+frT3G+/hTvsiO0PCM3Z8ydRguWlZJTeAP5JUvIL1kCW658R8eFS6EpsUeGjlrONnribWsKfxFxlc5LLXReauH4m2+Fls2Ymcr84sVjnkkoWFpMZl4umXm5LL/nztC6A719nK87SVNtfehMoam2nsG+Pie6YxuFv4i43mBfP2ePHOXskaOhZT6fj6yFC8bcflqwtJi5+fMpXLWCwlUrQusGAgFaPzg3Zkrs5hN1Yb03OlYp/EXEk4wxtH7QSOsHjRze9Vpo+cyMjNBwkTV8VELekiJyC28gt/AGVj3yQGjdnssdo6bEts4U4uVtagp/EZFR+jo7OVn1Lier3g0tS0xKYt7iRWOmxA69Te22ckpuu9bb1OrHvCuhtyO23qam8BcRmYB/eJjm2nqaa+upeeHK8tFvUxt5ec713qZ2+fwF613Loya9a/3AubepKfxFRCI07tvUQjuFYvJLipkzP4858/M+9Da183WnxjyT0Fx3Miq1K/xFRKZRuG9TK1hawpy8eSxavZJFq1eG1g34/fR2239nkcJfRMRm132b2pzMsbefLrPepuaLwnMGCn8REYf0XO6g7u1q6t6uDi1LTE7m7yu/a/u2E2zfgoiIhM0/NETCkP23iir8RUQ8SOEvIuJBCn8REQ9S+IuIeJDCX0TEgxT+IiIepPAXEfEgn1OTCoXhEtAQ4WdzgJZprCUeqM/eoD57w1T6XAjkTrRSLIf/VFQD5ROu5S7qszeoz95ge5817CMi4kEKfxERD3Jr+H/P6QIcoD57g/rsDbb32a1j/iIiMg63HvmLiMg4FP4iIh6k8BcR8SC3hH8CkBn883rgXudKiZpHgUSnixCRabEt2ht0y2scfwUUAPXAHKAV2Aj8DyeLstlvAX8HvAL8FKhytpyouAf4BnAB+K9As7PlRMXVBzJtwPvXWlHi2oRP5E43t9zt8yZWMJwBFgN+4FXgIQdrioZkYAPwH4FlwL8Bf+1oRfZ6A/hdrB39k8AnnC0nKpqB7YAv+Hs+UAc85VhFzrgfKyD/xelCbNLH2IMZH2Cw8swWbjnyTwBuAJqwgsEHzHS0ougYAl4EzmKF4p/g7vAfAk4F25cdriVajgOfvmrZG04U4rA/xjrAcWv4v4W1g4sat4T/APAjoB/4cXDZoHPlRMVyrCP+jwLngZ9h7QDdbDnwA6yd+4rgn+HD4ehmCcAMp4twwGO4J6+u5Ui0N+iWYZ9rGTltcqt3scb6f4F1xuMF911n+etRrSK6UrEOakYkYs34eMGZciSKHgR22/Xlbgn/u4DHga8BAazx4E8DS50sKgoex7rW4cO68LvT2XKiYivWRVD1Wdzkr4HfAf4zsAj4I6wbWH7Drg265TTqG8A/Yh0Fzwv+vNvRiuz3JWAt8M9YofB5rItD33WyKJt9CSjDGuIa6XMRUOlkUTb7U6x/59F9dvu/sxfdB9yMdTF/G7AJOGfnBt1y5L8b6xTpMHA70OtsOVHxJtb/MP7g7ynALtz9jIP67I0+e9G/Yx35/1/GHu2ftWvm1y0QAAADMUlEQVSDbjnyXwvswbrg+eKo5Q84U05UDHIlEMC66O32i9zqszf67EWDwA+BzuDPkWuWtmWYW478C6+zPNLXQMaDfsZe6E0EFuLup37VZ2/02Yui/gCjW478+4CLjL3DZ55z5URFavDnHcDHgN9k7FmPG6nP3uizF/0N8J+wnlP6BlF4gNEtc/v8Ivhz9zWWudXfYD0A9GfAQayLQ1sdrch+6rM3+uxFIw8wvgnMj8YG3RL+I3wTr+Ian8E6AvzvWA87DTlbTlSoz97osxeNPMD4T1x5gPEH435iitwy7JMPfBJrj/lJrJ1AVPaeDlqAdZ//M1jXPDKAEqxbxdxKffZGn73ot0f9+YfR2KBbLvhWY01+NXzV8q84UIsTcrD+5/lt3P98wwj1WWQK3BL+PwTuxNoBeGV6YxGRiLkl/MF70xuLiETMTeE/4has6Y1/H2t8VEREruKWC75enN5YRCRibjny9+L0xiIiEXNL+IuIyCS47SEvEREJg8JfRMSDFP4iU7cdOAM84WwZIuFT+IuXpAI/Al4F9mK9PGM6bCJKj+SLTBe33OopEo6NWNN/PzRq2RbgT7AOhC4CvwX8OdacOiuBA1gTbT2MtcN4ByjHmjp8K9B9nW0twnrVYhrWHWifwJqU7Q+xpmX2Ac8CP5merolMjo78xUvexAryv8R69y9YQzZ3Y00PkoD1VjiAo0At8AZjD5JOYL1dqYbxX679t8BfYL1usRFrpwLwB1hz89yLgl8cpCN/8ZIWrDcmPQD8I7ADax6ov8CaHmQ51pE6WHPnr8J6hmT0VOEj74w4yfgzx67AeikHwCyuvFXuU1hH/IPA01jvnRaJOoW/eI3BCvBTwK+wpkv+PPA+8NIkvuc+xh/nPwl8GesMYbT9wGPAXcD/Ah6cxDZFpo3CX7zkY0AF1gvRfcCfAjcB38cal796SvBreR7owBpC2gnkAr/EGuPvx9opfArrOsJ3gADWWP/nsYaS3gxuPwn4u2nplUgE9ISvSPj2Yt3OecbRKkSmgS74ioh4kI78RUQ8SEf+IiIepPAXEfEghb+IiAcp/EVEPEjhLyLiQQp/EREP+v/h0qq1pwA4agAAAABJRU5ErkJggg==\n",
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\n"
     },
     "metadata": {
      "needs_background": "dark"
     }
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d6d9b0>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "# 观察 often 后面的词汇\n",
    "tags = [\n",
    "        b[1]\n",
    "        for (a, b) in nltk.bigrams(brown_learned_tagged)\n",
    "        if a[0] == 'often'\n",
    "]\n",
    "fd = nltk.FreqDist(tags)\n",
    "fd.tabulate()\n",
    "fd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "combined to achieve\ncontinue to place\nserve to protect\nwanted to wait\nallowed to place\nexpected to become\nexpected to approve\nexpected to make\nintends to make\nseek to set\nlike to see\n"
     ]
    }
   ],
   "source": [
    "# P205 Ex5-2 使用POS标记寻找三词短语(<Verb>to<Verb>)\n",
    "def process(sentence):\n",
    "    for (w1, t1), (w2, t2), (w3, t3) in nltk.trigrams(sentence):\n",
    "        if t1.startswith('V') and t2 == 'TO' and t3.startswith('V'):\n",
    "            print(w1, w2, w3)\n",
    "\n",
    "\n",
    "for i, tagged_sent in enumerate(brown.tagged_sents()):\n",
    "    if i < 100 and len(tagged_sent) >= 3:\n",
    "        process(tagged_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "best ADJ ADV VERB NOUN\nclose ADV ADJ VERB NOUN\nopen ADJ VERB NOUN ADV\npresent ADJ ADV NOUN VERB\nthat ADP DET PRON ADV\n"
     ]
    }
   ],
   "source": [
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "data = nltk.ConditionalFreqDist((word.lower(), tag) for (word, tag) in brown_news_tagged)\n",
    "for word in sorted(data.conditions()):\n",
    "    if len(data[word]) > 3:\n",
    "        tags = [\n",
    "                tag\n",
    "                for (tag, _) in data[word].most_common()\n",
    "        ]\n",
    "        print(word, ' '.join(tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "nltk.app.concordance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "data['works']=  <FreqDist with 2 samples and 9 outcomes>\ndata['$1']=  <FreqDist with 1 samples and 5 outcomes>\ndata['$222']=  <FreqDist with 0 samples and 0 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(\"data['works']= \", data['works'])\n",
    "print(\"data['$1']= \", data['$1'])\n",
    "print(\"data['$222']= \", data['$222'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >data.conditions()< ---------------\n['the', 'fulton', 'county', 'grand', 'jury', 'said', 'friday', 'an', 'investigation', 'of', \"atlanta's\", 'recent', 'primary', 'election', 'produced', '``', 'no', 'evidence', \"''\", 'that']\n"
     ]
    }
   ],
   "source": [
    "# data.tabulate()  # 执行结果过多，时间过长\n",
    "show_subtitle(\"data.conditions()\")\n",
    "print(data.conditions()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=============== >data.values()< ===============\n--------------- >the< ---------------\n6386,\n--------------- >fulton< ---------------\n14,\n--------------- >county< ---------------\n61,\n--------------- >grand< ---------------\n18,1,\n--------------- >jury< ---------------\n46,\n--------------- >said< ---------------\n406,\n--------------- >friday< ---------------\n41,\n--------------- >an< ---------------\n311,\n--------------- >investigation< ---------------\n11,\n--------------- >of< ---------------\n2861,\n--------------- >atlanta's< ---------------\n4,\n--------------- >recent< ---------------\n20,\n--------------- >primary< ---------------\n13,4,\n--------------- >election< ---------------\n41,\n--------------- >produced< ---------------\n6,\n--------------- >``< ---------------\n732,\n--------------- >no< ---------------\n112,8,\n--------------- >evidence< ---------------\n17,\n--------------- >''< ---------------\n702,\n--------------- >that< ---------------\n546,128,150,5,\n"
     ]
    }
   ],
   "source": [
    "show_title(\"data.values()\")\n",
    "# print(data.values())  # 执行结果过多，时间过长\n",
    "for i, (cfd_key, cfd_value) in enumerate(zip(data.keys(), data.values())):\n",
    "    if i < 20:\n",
    "        show_subtitle(cfd_key)\n",
    "        for j, fd_value in enumerate(cfd_value.values()):\n",
    "            print(fd_value, end=',')\n",
    "        print()"
   ]
  }
 ]
}